Overview

Dataset statistics

Number of variables13
Number of observations5497
Missing cells0
Missing cells (%)0.0%
Duplicate rows745
Duplicate rows (%)13.6%
Total size in memory730.3 KiB
Average record size in memory136.0 B

Variable types

Numeric12
Categorical1

Alerts

Dataset has 745 (13.6%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with typeHigh correlation
volatile acidity is highly overall correlated with typeHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
type is highly overall correlated with fixed acidity and 3 other fieldsHigh correlation
citric acid has 136 (2.5%) zerosZeros

Reproduction

Analysis started2023-03-15 04:05:19.012841
Analysis finished2023-03-15 04:06:09.591213
Duration50.58 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8189922
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:09.698542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87031102
Coefficient of variation (CV)0.14956388
Kurtosis0.24756023
Mean5.8189922
Median Absolute Deviation (MAD)1
Skewness0.16294871
Sum31987
Variance0.75744127
MonotonicityNot monotonic
2023-03-15T13:06:09.871754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2416
44.0%
5 1788
32.5%
7 924
 
16.8%
4 186
 
3.4%
8 152
 
2.8%
3 26
 
0.5%
9 5
 
0.1%
ValueCountFrequency (%)
3 26
 
0.5%
4 186
 
3.4%
5 1788
32.5%
6 2416
44.0%
7 924
 
16.8%
8 152
 
2.8%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 152
 
2.8%
7 924
 
16.8%
6 2416
44.0%
5 1788
32.5%
4 186
 
3.4%
3 26
 
0.5%

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2101146
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:10.106498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2875791
Coefficient of variation (CV)0.17857955
Kurtosis5.1358213
Mean7.2101146
Median Absolute Deviation (MAD)0.6
Skewness1.7108972
Sum39634
Variance1.6578598
MonotonicityNot monotonic
2023-03-15T13:06:10.404731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 292
 
5.3%
6.6 281
 
5.1%
6.4 248
 
4.5%
7 247
 
4.5%
6.9 243
 
4.4%
7.2 229
 
4.2%
6.7 224
 
4.1%
7.1 218
 
4.0%
7.4 204
 
3.7%
6.5 195
 
3.5%
Other values (96) 3116
56.7%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 1
 
< 0.1%
4.4 3
 
0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 8
 
0.1%
4.9 8
 
0.1%
5 25
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 1
< 0.1%
15.5 2
< 0.1%
15 2
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct179
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33816263
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:10.687336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.16322431
Coefficient of variation (CV)0.48267991
Kurtosis2.9049594
Mean0.33816263
Median Absolute Deviation (MAD)0.08
Skewness1.4978441
Sum1858.88
Variance0.026642175
MonotonicityNot monotonic
2023-03-15T13:06:10.969926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 240
 
4.4%
0.24 240
 
4.4%
0.26 218
 
4.0%
0.27 197
 
3.6%
0.2 191
 
3.5%
0.22 190
 
3.5%
0.25 190
 
3.5%
0.23 185
 
3.4%
0.3 181
 
3.3%
0.32 174
 
3.2%
Other values (169) 3491
63.5%
ValueCountFrequency (%)
0.08 3
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 5
 
0.1%
0.105 6
 
0.1%
0.11 9
 
0.2%
0.115 3
 
0.1%
0.12 32
0.6%
0.125 3
 
0.1%
0.13 38
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.04 2
< 0.1%
1.035 1
< 0.1%

citric acid
Real number (ℝ)

Distinct89
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31854284
Minimum0
Maximum1.66
Zeros136
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:11.283762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.048
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.14510388
Coefficient of variation (CV)0.45552391
Kurtosis2.7270351
Mean0.31854284
Median Absolute Deviation (MAD)0.07
Skewness0.49248572
Sum1751.03
Variance0.021055136
MonotonicityNot monotonic
2023-03-15T13:06:11.550759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 287
 
5.2%
0.28 257
 
4.7%
0.32 248
 
4.5%
0.49 239
 
4.3%
0.34 211
 
3.8%
0.29 208
 
3.8%
0.26 206
 
3.7%
0.31 204
 
3.7%
0.27 193
 
3.5%
0.24 190
 
3.5%
Other values (79) 3254
59.2%
ValueCountFrequency (%)
0 136
2.5%
0.01 31
 
0.6%
0.02 49
 
0.9%
0.03 26
 
0.5%
0.04 33
 
0.6%
0.05 19
 
0.3%
0.06 28
 
0.5%
0.07 23
 
0.4%
0.08 30
 
0.5%
0.09 37
 
0.7%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 1
 
< 0.1%
0.8 2
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct309
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4380753
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:11.848467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.14
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7566762
Coefficient of variation (CV)0.87469847
Kurtosis5.1576372
Mean5.4380753
Median Absolute Deviation (MAD)1.7
Skewness1.4989539
Sum29893.1
Variance22.625968
MonotonicityNot monotonic
2023-03-15T13:06:12.303586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 193
 
3.5%
1.4 189
 
3.4%
2 188
 
3.4%
1.6 182
 
3.3%
1.2 170
 
3.1%
2.2 155
 
2.8%
1.7 152
 
2.8%
2.1 147
 
2.7%
1.5 141
 
2.6%
1.9 133
 
2.4%
Other values (299) 3847
70.0%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 6
 
0.1%
0.8 23
 
0.4%
0.9 33
 
0.6%
0.95 4
 
0.1%
1 81
1.5%
1.05 1
 
< 0.1%
1.1 125
2.3%
1.15 3
 
0.1%
1.2 170
3.1%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%
20.2 2
< 0.1%

chlorides
Real number (ℝ)

Distinct205
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055808077
Minimum0.009
Maximum0.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:12.586442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.064
95-th percentile0.1002
Maximum0.61
Range0.601
Interquartile range (IQR)0.026

Descriptive statistics

Standard deviation0.034653306
Coefficient of variation (CV)0.62093711
Kurtosis45.986221
Mean0.055808077
Median Absolute Deviation (MAD)0.011
Skewness5.1873268
Sum306.777
Variance0.0012008516
MonotonicityNot monotonic
2023-03-15T13:06:12.869054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 173
 
3.1%
0.036 171
 
3.1%
0.042 160
 
2.9%
0.048 160
 
2.9%
0.04 155
 
2.8%
0.046 155
 
2.8%
0.034 149
 
2.7%
0.038 148
 
2.7%
0.045 146
 
2.7%
0.047 145
 
2.6%
Other values (195) 3935
71.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 3
 
0.1%
0.013 1
 
< 0.1%
0.014 2
 
< 0.1%
0.015 3
 
0.1%
0.016 5
 
0.1%
0.017 4
 
0.1%
0.018 7
0.1%
0.019 7
0.1%
0.02 13
0.2%
ValueCountFrequency (%)
0.61 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
0.1%
0.414 2
< 0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%
0.387 1
 
< 0.1%
0.369 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct127
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.417682
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:13.166758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile60.1
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.673881
Coefficient of variation (CV)0.58103971
Kurtosis9.4003314
Mean30.417682
Median Absolute Deviation (MAD)12
Skewness1.310631
Sum167206
Variance312.36608
MonotonicityNot monotonic
2023-03-15T13:06:13.449340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 147
 
2.7%
15 140
 
2.5%
6 138
 
2.5%
31 135
 
2.5%
26 135
 
2.5%
17 135
 
2.5%
23 124
 
2.3%
35 121
 
2.2%
28 118
 
2.1%
36 117
 
2.1%
Other values (117) 4187
76.2%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
3 51
 
0.9%
4 45
 
0.8%
5 108
2.0%
5.5 1
 
< 0.1%
6 138
2.5%
7 84
1.5%
8 80
1.5%
9 80
1.5%
ValueCountFrequency (%)
289 1
 
< 0.1%
146.5 1
 
< 0.1%
138.5 1
 
< 0.1%
131 1
 
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
118.5 1
 
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
108 3
0.1%

total sulfur dioxide
Real number (ℝ)

Distinct271
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.56649
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:13.747579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q178
median118
Q3155
95-th percentile205
Maximum440
Range434
Interquartile range (IQR)77

Descriptive statistics

Standard deviation56.288223
Coefficient of variation (CV)0.48706353
Kurtosis-0.3277302
Mean115.56649
Median Absolute Deviation (MAD)38
Skewness0.0026007102
Sum635269
Variance3168.3641
MonotonicityNot monotonic
2023-03-15T13:06:14.014102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 63
 
1.1%
113 54
 
1.0%
128 50
 
0.9%
114 50
 
0.9%
122 50
 
0.9%
110 47
 
0.9%
98 47
 
0.9%
124 47
 
0.9%
101 46
 
0.8%
118 46
 
0.8%
Other values (261) 4997
90.9%
ValueCountFrequency (%)
6 3
 
0.1%
7 2
 
< 0.1%
8 9
 
0.2%
9 15
0.3%
10 24
0.4%
11 24
0.4%
12 25
0.5%
13 23
0.4%
14 28
0.5%
15 26
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
282 1
< 0.1%
272 1
< 0.1%
259 1
< 0.1%

density
Real number (ℝ)

Distinct970
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99467335
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:14.296991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.9923
median0.9948
Q30.99693
95-th percentile0.9994
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00463

Descriptive statistics

Standard deviation0.0030138844
Coefficient of variation (CV)0.0030300243
Kurtosis7.8065213
Mean0.99467335
Median Absolute Deviation (MAD)0.0023
Skewness0.60411922
Sum5467.7194
Variance9.0834991 × 10-6
MonotonicityNot monotonic
2023-03-15T13:06:14.595235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 61
 
1.1%
0.9928 57
 
1.0%
0.9976 54
 
1.0%
0.998 53
 
1.0%
0.992 52
 
0.9%
0.9956 51
 
0.9%
0.9986 50
 
0.9%
0.9962 48
 
0.9%
0.9958 47
 
0.9%
0.9932 47
 
0.9%
Other values (960) 4977
90.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
< 0.1%
1.0103 2
< 0.1%
1.00369 2
< 0.1%
1.0032 1
< 0.1%
1.00315 2
< 0.1%
1.00295 2
< 0.1%
1.00289 1
< 0.1%
1.0026 1
< 0.1%
1.00242 2
< 0.1%
1.00241 1
< 0.1%

pH
Real number (ℝ)

Distinct107
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2195015
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:14.881655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.27
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.16071281
Coefficient of variation (CV)0.049918539
Kurtosis0.38457996
Mean3.2195015
Median Absolute Deviation (MAD)0.11
Skewness0.38785621
Sum17697.6
Variance0.025828609
MonotonicityNot monotonic
2023-03-15T13:06:15.180181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 178
 
3.2%
3.22 157
 
2.9%
3.14 155
 
2.8%
3.2 153
 
2.8%
3.18 148
 
2.7%
3.19 143
 
2.6%
3.15 139
 
2.5%
3.24 136
 
2.5%
3.1 130
 
2.4%
3.26 129
 
2.3%
Other values (97) 4029
73.3%
ValueCountFrequency (%)
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 2
 
< 0.1%
2.8 3
 
0.1%
2.82 1
 
< 0.1%
2.83 3
 
0.1%
2.84 1
 
< 0.1%
2.85 6
0.1%
2.86 10
0.2%
2.87 8
0.1%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 1
< 0.1%
3.77 1
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct106
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53052392
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:15.462809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14939585
Coefficient of variation (CV)0.2816006
Kurtosis9.7434818
Mean0.53052392
Median Absolute Deviation (MAD)0.08
Skewness1.915479
Sum2916.29
Variance0.022319121
MonotonicityNot monotonic
2023-03-15T13:06:15.760502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 237
 
4.3%
0.46 201
 
3.7%
0.54 198
 
3.6%
0.44 187
 
3.4%
0.48 182
 
3.3%
0.38 181
 
3.3%
0.52 178
 
3.2%
0.49 167
 
3.0%
0.47 165
 
3.0%
0.53 163
 
3.0%
Other values (96) 3638
66.2%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.25 4
 
0.1%
0.26 3
 
0.1%
0.27 11
 
0.2%
0.28 10
 
0.2%
0.29 16
 
0.3%
0.3 25
0.5%
0.31 32
0.6%
0.32 51
0.9%
0.33 49
0.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct103
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.504918
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-03-15T13:06:16.058738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1945237
Coefficient of variation (CV)0.11371089
Kurtosis-0.54721713
Mean10.504918
Median Absolute Deviation (MAD)0.9
Skewness0.55359817
Sum57745.537
Variance1.4268868
MonotonicityNot monotonic
2023-03-15T13:06:16.342907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 303
 
5.5%
9.4 285
 
5.2%
9.2 229
 
4.2%
10 197
 
3.6%
10.5 197
 
3.6%
11 183
 
3.3%
9.8 180
 
3.3%
9.3 172
 
3.1%
9 172
 
3.1%
10.4 159
 
2.9%
Other values (93) 3420
62.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 3
 
0.1%
8.5 7
 
0.1%
8.6 18
 
0.3%
8.7 70
1.3%
8.8 88
1.6%
8.9 82
1.5%
9 172
3.1%
9.05 1
 
< 0.1%
9.1 138
2.5%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 10
0.2%
13.9 3
 
0.1%
13.8 2
 
< 0.1%
13.7 6
0.1%
13.6 11
0.2%
13.55 1
 
< 0.1%
13.5 11
0.2%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
white
4159 
red
1338 

Length

Max length5
Median length5
Mean length4.513189
Min length3

Characters and Unicode

Total characters24809
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowred
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 4159
75.7%
red 1338
 
24.3%

Length

2023-03-15T13:06:16.623505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-15T13:06:16.890946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
white 4159
75.7%
red 1338
 
24.3%

Most occurring characters

ValueCountFrequency (%)
e 5497
22.2%
w 4159
16.8%
h 4159
16.8%
i 4159
16.8%
t 4159
16.8%
r 1338
 
5.4%
d 1338
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24809
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5497
22.2%
w 4159
16.8%
h 4159
16.8%
i 4159
16.8%
t 4159
16.8%
r 1338
 
5.4%
d 1338
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 24809
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5497
22.2%
w 4159
16.8%
h 4159
16.8%
i 4159
16.8%
t 4159
16.8%
r 1338
 
5.4%
d 1338
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5497
22.2%
w 4159
16.8%
h 4159
16.8%
i 4159
16.8%
t 4159
16.8%
r 1338
 
5.4%
d 1338
 
5.4%

Interactions

2023-03-15T13:06:05.993046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:34.612867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:37.582674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:40.373124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:43.238138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:46.214661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:48.999833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:51.788642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:54.754028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:57.440633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:00.180708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:02.959348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:06.228069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:35.031877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:37.813869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:40.610744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:43.473097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:46.445125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:49.231681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:52.019287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:54.976394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:57.667225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:00.410729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:03.358268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:06.462095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:35.263281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:38.043925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:40.848359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:43.705871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:46.676219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:49.463426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:52.251883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:55.199607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:57.894716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:00.642265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:03.608835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:06.710274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:35.504613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:38.284649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:41.096209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:43.948561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:46.917542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:49.705648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:52.493966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:55.432169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:58.133324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:00.882060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:03.856214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:06.946987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:35.734663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:38.520346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:41.336208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:44.356980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:47.150623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:49.937710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:52.726973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:55.658650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:58.361757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:01.115300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:04.094250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:07.184978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:35.965871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:38.750375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:41.575217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:44.587923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:47.381989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:50.168985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:52.959038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:55.881022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:58.588803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:01.346232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:04.330706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:07.422152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:36.198156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:38.983727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:41.812930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:44.821494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:47.614232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:50.402364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:53.193184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:56.104335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:58.816429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:01.579270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:04.571419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:07.656737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:36.429618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:39.216141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:42.051213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:45.054690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:47.845706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:50.634421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:53.425346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:56.327398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:59.044863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:01.810450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:04.810925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:07.878173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:36.645124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:39.435096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:42.274721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:45.272322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:48.062378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:50.849807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:53.808292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:56.533848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:59.257318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:02.026492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:05.032873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:08.107291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:36.869205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:39.661514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:42.506120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:45.499255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:48.288659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:51.077431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:54.030846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:56.750278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:59.478734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:02.251642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:05.264538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:08.343043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:37.100872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:39.892333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:42.744986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:45.732251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:48.520158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:51.308471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:54.273678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:56.974746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:59.706990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:02.482038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:05.502230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:08.586686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:37.345062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:40.134024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:42.991920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:45.975271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:48.761163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:51.549649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:54.513963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:57.209819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:05:59.944386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:02.722330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-15T13:06:05.748349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-15T13:06:17.079356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
qualityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholtype
quality1.000-0.097-0.2520.099-0.014-0.2920.089-0.052-0.3210.0330.0350.4440.134
fixed acidity-0.0971.0000.1900.279-0.0380.353-0.257-0.2290.430-0.2500.218-0.1110.504
volatile acidity-0.2520.1901.000-0.290-0.0630.407-0.365-0.3460.2550.1920.248-0.0170.664
citric acid0.0990.279-0.2901.0000.067-0.0780.1140.1520.058-0.2770.0310.0270.420
residual sugar-0.014-0.038-0.0630.0671.000-0.0350.3860.4590.526-0.225-0.135-0.3330.348
chlorides-0.2920.3530.407-0.078-0.0351.000-0.255-0.2610.5920.1670.376-0.4060.766
free sulfur dioxide0.089-0.257-0.3650.1140.386-0.2551.0000.7370.007-0.165-0.221-0.1890.419
total sulfur dioxide-0.052-0.229-0.3460.1520.459-0.2610.7371.0000.069-0.240-0.255-0.3150.798
density-0.3210.4300.2550.0580.5260.5920.0070.0691.0000.0200.275-0.7030.322
pH0.033-0.2500.192-0.277-0.2250.167-0.165-0.2400.0201.0000.2530.1340.340
sulphates0.0350.2180.2480.031-0.1350.376-0.221-0.2550.2750.2531.0000.0010.472
alcohol0.444-0.111-0.0170.027-0.333-0.406-0.189-0.315-0.7030.1340.0011.0000.145
type0.1340.5040.6640.4200.3480.7660.4190.7980.3220.3400.4720.1451.000

Missing values

2023-03-15T13:06:08.944205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-15T13:06:09.393090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

qualityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholtype
df_index
055.60.6950.066.80.0429.084.00.994323.440.4410.2white
158.80.6100.142.40.06710.042.00.996903.190.599.5red
257.90.2100.392.00.05721.0138.00.991763.050.5210.9white
367.00.2100.316.00.04629.0108.00.993903.260.5010.8white
467.80.4000.269.50.05932.0178.00.995503.040.4310.9white
566.00.1900.379.70.03217.050.00.993203.080.6612.0white
656.10.2200.491.50.05118.087.00.992803.300.469.6white
767.10.3800.4211.80.04132.0193.00.996243.040.4910.0white
856.80.2400.3118.30.04640.0142.01.000003.300.418.7white
956.80.3900.3511.60.04457.0220.00.997753.070.539.3white
qualityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholtype
df_index
548757.30.590.267.20.07035.0121.00.998103.370.499.4red
548857.60.270.321.20.04323.072.00.992363.060.6810.5white
548965.10.390.211.70.02715.072.00.989403.500.4512.5white
5490611.10.450.733.20.0666.022.00.998603.170.6611.2red
549176.90.340.304.70.02934.0148.00.991653.360.4912.3white
549257.70.150.291.30.02910.064.00.993203.350.3910.1white
549366.30.180.361.20.03426.0111.00.990743.160.5111.0white
549477.80.150.341.10.03531.093.00.990963.070.7211.3white
549556.60.410.311.60.04218.0101.00.991953.130.4110.5white
549667.00.350.171.10.0497.0119.00.992973.130.369.7white

Duplicate rows

Most frequently occurring

qualityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholtype# duplicates
64576.80.180.3012.80.06219.0171.00.998083.000.529.0white6
68877.40.160.2715.50.05025.0135.00.998402.900.438.7white6
68977.40.160.3013.70.05633.0168.00.998252.900.448.7white6
73987.30.190.2713.90.05745.0155.00.998072.940.418.8white6
74187.60.200.3014.20.05653.0212.50.999003.140.468.9white6
36666.70.160.3212.50.03518.0156.00.996662.880.369.0white5
48367.40.190.3114.50.04539.0193.00.998603.100.509.2white5
65977.00.150.2814.70.05129.0149.00.997922.960.399.0white5
69077.40.190.3012.80.05348.5229.00.998603.140.499.1white5
10356.90.400.1712.90.03359.0186.00.997543.080.499.4white4